Making Sense of Big Data, Part II: Sourcing, Synthesizing – and Creating Success
For equipment dealers, your challenges and goals have not really changed. You need to maintain a customer base, manage a territory, and target your competitors’ customers.
How can dealers benefit from Big Data? One way is to look at how Big Data has become hyperlocal. Broadcasters and content creators have known this for years. CNN and ESPN have developed networks within their respective networks to get you a channel that fits your desires. Local newspapers have come to realize that people read the news that’s about their sub-region or neighborhood, and they’ve molded their content outlets to reach these readers on their terms.
Dealers can use hyperlocal information to inform and train sales and services people. These folks have the “street knowledge” of practical experience as well as hard data, yet we can supplement that and make more sense of it for them. Hyperlocal data can help you and them grow sales, target qualified prospects, anticipate customers’ purchasing cycles, assess competitor market share, and guide strategic planning through exploiting product opportunities, trends and emerging markets.
The flip side of the old adage garbage in, garbage out is quality data in, quality insight out. This is a key starting point for making Big Data work for us. Low quality data is readily available. We can buy e-mail addresses and direct mailing lists all day long on the Internet. We can spend lots of time comparing, validating and scrubbing low quality data. That takes a lot of time and labor, and you lose most of the value because you’re working with inexpensive, “dirty” information. Who can afford to hire a “data scientist” to constantly clean up mass amounts of information to begin to make it useful.
Over the past 25-plus years, EDA has figured out the gathering, cleaning and producing insight parts of Big Data. Why recreate the wheel?
Let’s look at lift trucks. Out of the six million-plus UCCs that EDA collects each year, we’re able to identify 345,000 lift truck buyers with 1.4-plus million lift truck units on-hand, at some place in the lease/ownership cycle. That’s a heckuva start on making things simpler for our customers who need this information. Then, we make that insight hyperlocal and accessible.
As we help our customers make sense of this data, we’re adding the value that Guy Kawasaki writes about. It’s where we “make meaning” from our part of your Big Data strategy.
Our Big Data is clean. We spent the first 20 years learning how to make sure of that. We have the processes down. In the last five years, we’ve learned how to make our Big Data even more valuable by providing insight to these hyperlocal trends that are going on around our customers. For example, we can say with confidence, “hey, this company down the street is more likely to buy now because they have leases expiring, because they are expanding, and because they’re becoming less and less brand loyal to the competitive brand.”
That’s how Big Data can provide hyperlocal insight and hyperlocal benefits to your business. Quality data in, synthesized with expertise, and creating instant insight and value to your business. As I’ve mentioned before, Big Data is a tool, but a tool only as valuable as its parts. Big Data is powerful, and can be a powerful tool for your business when you’ve taken the best possible approach to it.
Stay tuned for part three of this series, in which we’ll help you make Big Data accessible, understandable and useful for your sales force.